Dear Fellow Scholars, this is Two Minute
Papers with Dr. Károly Zsolnai-Fehér. Today you will see an absolute banger paper. This
is about how amazingly NVIDIA’s virtual characters can move around after they have trained for 10
years. 10 years? We don’t have 10 years for a project! Well, luckily, we don’t have to wait for
10 years. Why is that? I will tell you exactly why in a moment. But, believe me, these folks are
not natural born warriors. They are AI agents that have to train for a long long time
to become so good! So, how does that work? Well, first, our candidates are fed a bunch of
basic motions, and then, are dropped into NVIDIA’s Isaac, which is a virtual gym where they can hone
their skills. But, unfortunately, they have none. After a week of training, I expected that they
would showcase some amazingly athletic warrior moves, but instead, we got… this. Oh my goodness.
Well, let’s be optimistic and say that they are practicing Judo where the first lesson is
learning how to fall. Yes, let’s say that. Then, after two months, we can witness some
improvement. Well, they are not falling, and they can do some basic movement.
But, they look like constipated warriors. After 2 years, we are starting to see something
that resembles true fight moves. These are not there yet, but they have improved a great deal.
Except this chap. This chap goes like “Sir, I’ve been training for two years, I’ve had enough! And
now, I shall leave… in style.” I wonder what these will look like in 8 more years of training. Well,
hold on to your papers, and let’s see together! Oh my! This is absolutely amazing! Now that’s
what I call a bunch of real fighters! See? Time is the answer! It made even our stylish chap take
its training seriously. So, which one is your favorite from here? Did you find some interesting
movements? Let me know in the comments below! Now, I promised that we will talk about the ten
year thing. So, did scientist at NVIDIA start this paper in 2012? Well, not quite. This is 10 years
of training, but in a virtual world. However, a real world computer simulates this virtual
world and it can do it much quicker than that. How much quicker? Well, a powerful machine
can simulate these 10 years not in 10 years, but in 10 days. Oh yes! Now
that sounds much better! And, we are not done yet. Not even close! When
reading this paper, I was so happy to find out that this new technique also
has four more amazing features. One, it works with latent spaces. What is
that? A latent space is a made up place where similar kinds of data are laid out to
be close to each other. In an earlier paper, we used such a space to generate beautiful
virtual materials for virtual worlds. NVIDIA uses a latent space to switch between the
motion types that the character now knows, and not only that, but their AI also
learned how to weave these motions together, even if they were not combined together
in the training data. That is incredible! Two, this is my favorite. It has to be. They
not only learned to fall, but in those 10 years, they had plenty of opportunity to also learn
to get up. Do you know what this means? Of course, this means the favorite pastime of
the computer graphics researcher. And that is, throwing boxes at virtual characters. We like to
say that we are testing whether the character can recover from random perturbations. That
sounds a little more scientific. And, these AI agents are passing with flying
colors. Or flying boxes, if you will. Wow. Three, the controls are excellent. Look. This really has some amazing
potential to be used in virtual worlds, because we can even have the character face
one way, and move into a different direction at the same time. More detailed poses can also
be specified. And, what’s more, with this, we can really enter a virtual environment and strike
down these evil pillars with precision. Loving it. Four, these motions are synthesized adversarially.
This means that we have a generator neural network creating these new kinds of motions. But, we
connect it to another neural network called the discriminator, this watches it and ensures that
the generated motions are similar to the ones in the dataset and seem real too. And, as they battle
each other, they also improve together, and in the end, we take only the motion types that are
good enough to fool the discriminator. Hopefully, these are good enough to fool the human eye too.
And, as you see, the results speak for themselves. If we wouldn’t be doing it this way, here is what we would get if we
trained these agents from scratch. And, yes, while we are talking about training.
This did not start out well at all. Imagine if scientists at NVIDIA quit after just 1 week of
training, which is about 30 minutes in real time. These results are not too promising, are they?
But, they still kept going. And the result was this! That is excellent life advice right there,
and, also, this is an excellent opportunity for us to invoke the The Third Law Of Papers.
Not the first, the third one! This says that a bad researcher fails 100% of the time,
while a good one only fails 99% of the time. Hence, what you see here is always
just 1% of the work that was done. And, this is done by NVIDIA, so I am sure that
we will see this deployed in real world projects, where these amazing agents will get democratized
by putting it into the hands of all of us. What a time to be alive! So,
does this get your mind going? What would you use this for? Let
me know in the comments below! Thanks for watching and for your generous
support, and I'll see you next time!
Probably did it with a montage, even Rocky had a montage
Okay... This is literally Naruto Uzumaki Training. Naruto literally makes a bunch of clones himself to parallel train to get stronger.